Artificial lighting is strongly associated with urbanisation and is increasing in its extent, brightness and spectral range. Changes in urban lighting have both positive and negative effects on city performance, yet little is known about how its character and magnitude vary across the urban landscape. A major barrier to related research, planning and governance has been the lack of lighting data at the city extent, particularly at a fine spatial resolution. Our aims were therefore to capture such data using aerial night photography and to undertake a case study of urban lighting. We present the finest scale multi-spectral lighting dataset available for an entire city and explore how lighting metrics vary with built density and land-use. We found positive relationships between artificial lighting indicators and built density at coarse spatial scales, whilst at a local level lighting varied with land-use. Manufacturing and housing are the primary land-use zones responsible for the city's brightly lit areas, yet manufacturing sites are relatively rare within the city. Our data suggests that efforts to address light pollution should broaden their focus from residential street lighting to include security lighting within manufacturing areas.

Steel is the most widely used metal in the world, and numerous studies have investigated its stock and flow. Two basic methods for analysing material flow and accounting for stock are the top-down and bottom-up approaches. Their applicability, however, largely depends on data availability. To overcome this limitation, we have contemplated using satellite imagery as a proxy for missing data. In a previous study, we confirmed the correlation between night-time light radiance and civil engineering/building in-use steel stocks in Japan. In this study, the scope of the investigation was expanded to a global scale, examining correlations in different regions of the world. We found that night-time light radiance and in-use steel stocks have region-specific linear correlations, which are influenced by construction styles, which in turn depend on climate, seismic activity, cultural preferences, etc. The results were then applied to countries in the various regions whose in-use steel stocks were previously unreported. This technique produced an estimate of the global civil engineering/building in-use steel stock of around 9 Ã 109 tonnes (9 Gt), with 1.24 Gt being previously unreported. As a further step, this study shows the spatial distribution of civil engineering/building in-use steel stock in each region.

In remote-sensing community, radiometric calibration of night-time light images has long been a problem, hindering change detection of images in different dates. Currently, an intercalibration model is regarded as the unique solution for the problem, but prior knowledge is needed to extract reference pixels with stable lights, which are hard to obtain in most of the applications. This study proposed an automatic algorithm to extract the reference pixels for convenient use of the intercalibration model, with an assumption that there are sufficient pixels with stable night-time lights in the multi-temporal images. To automatically extract the stable pixels from images in two dates, all pixels in the two dates were entered into a linear regression model, and the outliers viewed as suspected changed pixels were discarded iteratively. Consequently, some stable pixels were extracted and the intercalibration model was implemented. Annual night-time light composites in Beijing, China, from 1992 to 2010 were taken as the study material, and the results show that the multi-temporal calibrated night-time light data have higher correlation with gross domestic production (GDP) (R 2&#8201;=&#8201;0.8734) and urban population (UP) (R 2&#8201;=&#8201;0.9269) than those of the uncalibrated images (with the R 2 values 0.7963 and 0.8575, respectively). Furthermore, the data inconsistency from different night-time light satellites in the same year was reduced with the proposed algorithm. The results demonstrate that the proposed algorithm is effective in intercalibrating the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) images automatically.

Zimbabweâs economy declined between 2000 and 2009. This study detects the economic decline in different regions of Zimbabwe using nighttime light imagery from the Defense Meteorological Satellite Programâs Operational Linescan System (DMSP-OLS). We found a good correlation (coefficient = 0.7361) between Zimbabweâs total nighttime light (TNL) and Gross Domestic Product (GDP) for the period 1992 to 2009. Therefore, TNL was used as an indicator of regional economic conditions in Zimbabwe. Nighttime light imagery from 2000 and 2008 was compared at both national and regional scales for four types of regions. At the national scale, we found that nighttime light in more than half of the lit area decreased between 2000 and 2008. Moreover, within the four region types (inland mining towns, inland agricultural towns, border towns and cities) we determined that the mining and agricultural sectors experienced the most severe economic decline. Some of these findings were validated by economic survey data, proving that the nighttime light data is a potential data source for detecting the economic decline in Zimbabwe.

Artificial night lights pose a major threat to multiple species. However, this threat is often disregarded in conservation management and action because it is difficult to quantify its effect. Increasing availability of high spatial-resolution satellite images may enable us to better incorporate this threat into future work, particularly in highly modified ecosystems such as the coastal zone. In this study we examine the potential of satellite night light imagery to predict the distribution of the endangered loggerhead (Caretta caretta) and green (Chelonia mydas) sea turtle nests in the eastern Mediterranean coastline. Using remote sensing tools and high resolution data derived from the SAC-C satellite and the International Space Station, we examined the relationship between the long term spatial patterns of sea turtle nests and the intensity of night lights along Israelâs entire Mediterranean coastline. We found that sea turtles nests are negatively related to night light intensity and are concentrated in darker sections along the coast. Our resulting GLMs showed that night lights were a significant factor for explaining the distribution of sea turtle nests. Other significant variables included: cliff presence, human population density and infrastructure. This study is one of the first to show that night lights estimated with satellite-based imagery can be used to help explain sea turtle nesting activity at a detailed resolution over large areas. This approach can facilitate the management of species affected by night lights, and will be particularly useful in areas that are inaccessible or where broad-scale prioritization of conservation action is required.